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This paper introduces a new fraud prediction model to the accounting literature using machine learning (ML). This model, which we refer to as LogitBoost, combines ensemble learning, one of the most powerful ML methods, and logistic regressions. We show, using seven alternative measures assessing the ability to detect fraud, that our model outperforms the methods based solely on logistic regressions or other ML methods used by prior literature. Additionally, our model outperforms the others in predicting fraud beyond the current accounting period. Importantly, our method relies on a lower number of predictors than those used in prior ML research, thus minimizing concerns over multicollinearity and potential overfitting associated with machine learning methods.
Original languageEnglish
Publication statusIn preparation - May 2022


  • machine learning
  • logistic regressions
  • accounting irregularities
  • AAERs


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